Building a hierarchy of events and topics for newspaper digital libraries

Abstract

In this paper we propose an incremental hierarchical clustering algorithm for on-line event detection. This algorithm is applied to a set of newspaper articles in order to discover the structure of topics and events that they describe. In the first level, articles with a high temporal-semantic similarity are clustered together into events. In the next levels of the hierarchy, these events are successively clustered so that composite events and topics can be discovered. The results obtained for the Fl-measure and the Detection Cost demonstrate the validity of our algorithm for on-line event detection tasks. © Springer-Verlag Berlin Heidelberg 2003.